Predicting metrical patterns in Spanish poetry with language models
This research addresses the problem of accurately identifying metrical patterns in Spanish poetry for computational linguistics and literary analysis, offering an incremental improvement by applying existing language models.
This paper compares existing automated metrical pattern identification systems for Spanish poetry against fine-tuned BERT-based language models. The results indicate that BERT models, despite their semantic origins, perform reasonably well in identifying metrical patterns in Spanish scansion.
In this paper, we compare automated metrical pattern identification systems available for Spanish against extensive experiments done by fine-tuning language models trained on the same task. Despite being initially conceived as a model suitable for semantic tasks, our results suggest that BERT-based models retain enough structural information to perform reasonably well for Spanish scansion.